• Investigate why documents fail at population scale • Own and evolve the accuracy measurement framework • Design fixes for pipeline logic errors • Collaborate with AI team on model architecture and ML best practices • Build an embedding-based entity matching service and an OCR correction layer • Set up ML pipeline orchestration and MLOps practices

Role Description We’re building the machine learning and optimisation systems that help Monzo provide fast, efficient and resilient customer support at scale. You’ll lead Machine Learning for our Workforce Management vertical: - Ensure Monzo has the right human support supply to meet customer demand. - Work on demand forecasting, real-time supply <> demand matching, problem routing and escalation, and predictive models. - Exciting opportunity for someone close to technical work, such as a Tech Lead, Team Lead, Senior Machine Learning Scientist or Senior Machine Learning Engineer. Not everything here is about LLMs. You’ll work on rigorous ML, forecasting, optimisation and operations research problems where quantitative thinking, pragmatic delivery and strong product judgement really matter. You’ll lead a growing team of Machine Learning Scientists, embedded in product squads working alongside: - Data scientists - Backend, mobile and web engineers - Product managers - User researchers - Designers - Operations specialists Qualifications - Recent experience as a hands-on Machine Learning practitioner (Senior IC, Tech Lead, Team Lead or ML manager). - Strong foundations in ML, statistics, forecasting, optimisation or operations research. - Experience building, shipping or owning ML systems in production. - Ability to explain complex ML ideas clearly to technical and non-technical people. - Enjoyment in coaching others; open to first-time management roles. - Energy in ambiguous problem spaces. - Focus on customer outcomes and building efficient systems. - Adaptable, curious and excited by quantitative problems. Requirements - Ability to move between technical detail and business context. - Experience in creating models that are useful, reliable, monitored and safe. - Coaching and developing Machine Learning Scientists through regular 1:1s, feedback and technical guidance. - Creating healthy ways of working for the team: clear priorities, strong technical standards, good documentation, sensible monitoring. - Contributing to the wider Machine Learning discipline at Monzo. Benefits - Salary: £113,200 - £153,200 + Incentive Awards tied to your performance. - Relocation assistance to the UK. - Visa sponsorship available. - Flexible working hours. - £1,000 learning budget each year for books, training courses and conferences. - Setup for remote work with Macbooks and additional support for home office setup. - Plus lots more!

Role Description We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines. Key Responsibilities - Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering - Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks - Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets - Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines - Work with structured and unstructured data at scale — including text, tabular, and time-series data - Monitor model performance in production and implement retraining and drift-detection strategies - Collaborate with engineering and product teams to translate data insights into actionable AI features - Document data schemas, model architectures, and pipeline logic clearly and thoroughly Qualifications - Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar) - Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets - Practical experience with model training, evaluation, hyperparameter tuning, and deployment - Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts - Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar) - Strong grasp of statistical concepts, data quality principles, and model performance metrics - Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered. Preferred Qualifications - Experience with distributed data processing frameworks (Spark, Dask, or similar) - Familiarity with vector databases and embedding-based retrieval systems - Background working with real-time or streaming data pipelines (Kafka, Flink, or similar) - Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) - Experience with data governance, lineage tracking, or compliance-aware data workflows Tools & Technology - Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy) - MLOps: MLflow, Weights & Biases, DVC, or equivalent - Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar - LLM platforms: Hugging Face, OpenAI, Anthropic, or similar - Cloud infrastructure: AWS, GCP, or Azure - Google Workspace, Slack, Zoom, and remote collaboration tools Please note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles. We connect top talent with vetted employers, competitive pay, and real growth opportunities.

Role Description Provision and manage virtual machines across multiple environments. Perform OS configuration, patching, and basic hardening across Linux and Windows systems. - Monitor VM health including CPU utilization, memory usage, disk performance, and service availability. - Assist with backup configuration and validation procedures. - Support restore testing activities for virtual systems. - Troubleshoot VM performance issues and boot related incidents. - Maintain VM inventory and infrastructure documentation. - Assist with operational monitoring and alert investigation. - Support environments that follow federal security and compliance requirements. - Collaborate with cloud engineers and infrastructure teams during system changes. Qualifications - Need CompTIA Security+ certification Requirements - DoD 8570 / 8140 Security+ or equivalent - 7-10 years of experience supporting enterprise infrastructure systems or virtual platforms - Bachelor's Degree